1. Field of the Invention
The embodiments described herein relate to image processing techniques. In particular, these embodiments relate to white balancing multicolored image data.
2. Related Art
Because of the tri-stimulus nature of human color perception, to reconstruct a color image of a scene it is typically necessary to recover at least three color components (typically red, green, and blue or cyan, magenta, and yellow) for each picture element (pixel) in the image. In such color imaging systems, white balance is a critical factor in perceived image quality. White balancing is a process of weighting the intensities of the individual color channels of a composite color system to achieve the greatest fidelity of the image compared to the original scene. An objective of white balancing is for a white object to be imaged with properly proportioned energies in component colors (e.g., red, green, and blue).
Red, green, and blue color channels of a typical electronic imaging device may not be in balance with one another. This imbalance is primarily due to the effects of ambient lighting. Scenes imaged under fluorescent lights may produce image pixel responses that are different from pixel responses of the same scenes imaged under incandescent light or sunlight. Also, although less likely, inaccuracies in the placement of color transmissive filters over an imaging sensor, variations in the circuitry of the imaging sensor that comprise the individual pixels, or variations in the analog-to-digital conversion circuits (assuming separate A/D circuits are used for each color stream) may introduce color imbalance.
Various methods have been employed to achieve white balance. One method involves an inclusion of a white balance sensor (which may separate from, or combined with, a primary imaging sensor) within the system. Here, a camera operator typically points the white balance sensor at a white reference surface to extract reference color information. In another method, the imaging system may attempt to perform white balancing based upon image data extracted from a natural scene.
A system that requires the camera operator to image a white reference area to achieve proper white balance can be cumbersome due to the effects of ambient lighting on color channel balance. The operator typically performs the manual white balancing operation every time the ambient light changes. Scene based white balancing provides a more user friendly approach by utilizing information extracted from the imaging sensors independent of image data generated by imaging a white reference surface. This scene based approach places the burden of extracted color information for white balancing on the image processing algorithms not on the camera operator.
One approach to scene based white balance assumes that the average pixel intensity value for each of the color channels throughout any given scene are equivalent. In an imaging system with red, green and blue color channels, for example, one channel is typically designated as the reference channel and the other two channels are balanced to the reference reference channel. The green channel is typically designated as the reference channel due to its greater spectral responsivity over the red and blue channels and its location between red and blue channels in the visible light spectrum. Two separate gain factors are then computed and applied to the intensity values of the red and blue channel pixels to bring them into balance with the reference green channel.
The assumption that the average pixel intensity value for each of the three channels are equal, however, is not always accurate. In a scene where the averages in the imaged object are slightly different, this assumption will cause white areas in the resulting image to take on a colored tint. Therefore, there is a need for a scene based white balancing system which provides a more accurate representation of the colors in an imaged object.